Study of non stationary mode flow of an abdominal aortic aneurysm.
Bibliographic record
Abstract
INTRODUCTION: Abdominal aortic aneurysm rupture is 95% lethal. Numerical simulation of Navier-Stokes equations allows seeing complex flow phenomenon in laminar state. METHOD: In this study, we work on a model using the finite element method with an actual 3D geometry. This geometry is from a point cloud obtained by a tomodensitometry scan of a typical patient. We consider rigid wall, homogeneous and Newtonian fluid. We impose four pulsative waveforms as entrance condition: two rest waveforms, two exercise waveforms. The four aver-age Reynolds numbers are 353 and 363 for the rest waveforms and 1058 and 1388 for the exercise waveforms. For the systolic peaks, the Reynolds numbers are 1639 and 1917 for the rest waveforms and 2800 and 2497 for the exercise waveforms. RESULTS: RESULTS show that during the systolic acceleration, vortices issued from the previous pulsation are pushed out and the flow reattached on the wall. During the systolic deceleration, a main vortex appears in the upper part of the aneurysm; it grows and moves to the center. During the diastole, the vortex sustains itself until the next pulsation for the exercise conditions. For the rest conditions, imposed oscillations during the diastole lead to secondary vortices. Pressure stays relatively constant in the aneurysm following the entrance conditions. DISCUSSION: These results on the flow and pressure repartition agreed with those found in the literature (1-6) validating in a first time our model. The next step of the study is the wall shear stress data exploitation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".